docstr: Args:
      in_channels (int): number of input channels
      layers (list): feature dimensions of each FCN layer
      dilation (int): dilation rate of kernel
      norm_layer (callable, optional): Module specifying the normalization layer to use. Default: None
docstr_tok: ["Args",":","in_channels","(","int",")",":","number","of","input","channels","layers","(","list",")",":","feature","dimensions","of","each","FCN","layer","dilation","(","int",")",":","dilation","rate","of","kernel","norm_layer","(","callable",",","optional",")",":","Module","specifying","the","normalization","layer","to","use",".","Default",":","None"]
code: def __init__(
      self,
      in_channels,
      layers,
      dilation,
      norm_layer: Optional[Callable[..., nn.Module]] = None,
  ):
      """
      Args:
          in_channels (int): number of input channels
          layers (list): feature dimensions of each FCN layer
          dilation (int): dilation rate of kernel
          norm_layer (callable, optional): Module specifying the normalization layer to use. Default: None
      """
      blocks = []
      next_feature = in_channels
      for layer_features in layers:
          blocks.append(
              misc_nn_ops.Conv2dNormActivation(
                  next_feature,
                  layer_features,
                  kernel_size=3,
                  stride=1,
                  padding=dilation,
                  dilation=dilation,
                  norm_layer=norm_layer,
              )
          )
          next_feature = layer_features
  
      super().__init__(*blocks)
      for layer in self.modules():
          if isinstance(layer, nn.Conv2d):
              nn.init.kaiming_normal_(
                  layer.weight, mode="fan_out", nonlinearity="relu"
              )
              if layer.bias is not None:
                  nn.init.zeros_(layer.bias)
  
imports: from collections import OrderedDict
  from typing import Any, Callable, Optional
  from torch import nn
  from torchvision.ops import MultiScaleRoIAlign
  from ...ops import misc
  from ...transforms._presets import ObjectDetection
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _COCO_CATEGORIES
  from .._utils import _ovewrite_value_param, handle_legacy_interface
  from ..resnet import resnet50, ResNet50_Weights
  from ._utils import overwrite_eps
  from .backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers
  from .faster_rcnn import _default_anchorgen, FasterRCNN, FastRCNNConvFCHead, RPNHead
label_desc: Use image node_id 1 to create a new MaskRCNNHeads object from inherited base classes: nn with example: obj = MaskRCNNHeads(in_channels, layers, dilation, norm_layer)